Literature DB >> 27518311

"Exposure Track"-The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution.

Marguerite Nyhan1,2, Sebastian Grauwin1, Rex Britter1, Bruce Misstear3, Aonghus McNabola3, Francine Laden2, Steven R H Barrett4, Carlo Ratti1.   

Abstract

Air pollution is now recognized as the world's single largest environmental and human health threat. Indeed, a large number of environmental epidemiological studies have quantified the health impacts of population exposure to pollution. In previous studies, exposure estimates at the population level have not considered spatially- and temporally varying populations present in study regions. Therefore, in the first study of it is kind, we use measured population activity patterns representing several million people to evaluate population-weighted exposure to air pollution on a city-wide scale. Mobile and wireless devices yield information about where and when people are present, thus collective activity patterns were determined using counts of connections to the cellular network. Population-weighted exposure to PM2.5 in New York City (NYC), herein termed "Active Population Exposure" was evaluated using population activity patterns and spatiotemporal PM2.5 concentration levels, and compared to "Home Population Exposure", which assumed a static population distribution as per Census data. Areas of relatively higher population-weighted exposures were concentrated in different districts within NYC in both scenarios. These were more centralized for the "Active Population Exposure" scenario. Population-weighted exposure computed in each district of NYC for the "Active" scenario were found to be statistically significantly (p < 0.05) different to the "Home" scenario for most districts. In investigating the temporal variability of the "Active" population-weighted exposures determined in districts, these were found to be significantly different (p < 0.05) during the daytime and the nighttime. Evaluating population exposure to air pollution using spatiotemporal population mobility patterns warrants consideration in future environmental epidemiological studies linking air quality and human health.

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Year:  2016        PMID: 27518311     DOI: 10.1021/acs.est.6b02385

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  16 in total

Review 1.  Community-based participatory research for the study of air pollution: a review of motivations, approaches, and outcomes.

Authors:  Adwoa Commodore; Sacoby Wilson; Omar Muhammad; Erik Svendsen; John Pearce
Journal:  Environ Monit Assess       Date:  2017-07-06       Impact factor: 2.513

2.  Geographic Information Science and the Analysis of Place and Health.

Authors:  Jeremy Mennis; Eun-Hye Enki Yoo
Journal:  Trans GIS       Date:  2018-04-02

Review 3.  Towards Personal Exposures: How Technology Is Changing Air Pollution and Health Research.

Authors:  A Larkin; P Hystad
Journal:  Curr Environ Health Rep       Date:  2017-12

Review 4.  Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine.

Authors:  Shengjie Lai; Andrea Farnham; Nick W Ruktanonchai; Andrew J Tatem
Journal:  J Travel Med       Date:  2019-05-10       Impact factor: 8.490

Review 5.  Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives.

Authors:  Alessio Fascista
Journal:  Sensors (Basel)       Date:  2022-02-25       Impact factor: 3.576

6.  The Canadian Urban Environmental Health Research Consortium - a protocol for building a national environmental exposure data platform for integrated analyses of urban form and health.

Authors:  Jeffrey R Brook; Eleanor M Setton; Evan Seed; Mahdi Shooshtari; Dany Doiron
Journal:  BMC Public Health       Date:  2018-01-08       Impact factor: 3.295

Review 7.  Advancing environmental exposure assessment science to benefit society.

Authors:  Andrew Caplin; Masoud Ghandehari; Chris Lim; Paul Glimcher; George Thurston
Journal:  Nat Commun       Date:  2019-03-15       Impact factor: 17.694

8.  Assessment of the Dynamic Exposure to PM2.5 Based on Hourly Cell Phone Location and Land Use Regression Model in Beijing.

Authors:  Junli Liu; Panli Cai; Jin Dong; Junshun Wang; Runkui Li; Xianfeng Song
Journal:  Int J Environ Res Public Health       Date:  2021-05-30       Impact factor: 3.390

9.  "Biophilic Cities": Quantifying the Impact of Google Street View-Derived Greenspace Exposures on Socioeconomic Factors and Self-Reported Health.

Authors:  Anna C O'Regan; Ruth F Hunter; Marguerite M Nyhan
Journal:  Environ Sci Technol       Date:  2021-06-23       Impact factor: 9.028

10.  The Impact of Activity-Based Mobility Pattern on Assessing Fine-Grained Traffic-Induced Air Pollution Exposure.

Authors:  Yizheng Wu; Guohua Song
Journal:  Int J Environ Res Public Health       Date:  2019-09-07       Impact factor: 3.390

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